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Current robotic studies are focused on the performance of specific tasks. However, such tasks cannot be generalized, and some special tasks, such as compliant and precise manipulation, fast and flexible response, and deep collaboration between humans and robots, cannot be realized. Brain-inspired intelligent robots imitate humans and animals, from inner mechanisms to external structures, through an integration of visual cognition, decision making, motion control, and musculoskeletal systems. This kind of robot is more likely to realize the functions that current robots cannot realize and become human friends. With the focus on the development of brain-inspired intelligent robots, this article reviews cutting-edge research in the areas of brain-inspired visual cognition, decision making, musculoskeletal robots, motion control, and their integration. It aims to provide greater insight into brain-inspired intelligent robots and attracts more attention to this field from the global research community.

The musculoskeletal robot is a promising direction of the next-generation robots. However, current control methods of musculoskeletal robots lack multi-tasks learning ability, great generalization, and biological plausibility. In this article, a motor-cortex-like recurrent neural network (RNN) and a reward modulated multi-tasks learning method are proposed. First, inspired by dynamic system hypothesis of motor cortex, the RNN is introduced to transform movement targets into muscle excitations. The condition that makes a RNN generate motor-cortex-like consistent population response is investigated. Second, a reward modulated multi-tasks learning method of such a RNN is proposed. In the experiments, the control of a musculoskeletal system is realized with multi-tasks learning ability, great generalization, and robustness for noises. Furthermore, the RNN and muscle excitations demonstrate motorcortex-like consistent population response and human-like muscle synergies respectively. Therefore, the proposed method has better performance and biological plausibility, and verifies the neural mechanisms in the robotic research.

Defects, which arise during the growth and processing of the wood, affect the visual and mechanical properties. To improve the utilization of wood with defects, we propose an image inpainting method that reconstructs the texture at defects. The result of image inpainting is visually very consistent with the surrounding healthy board. According to the result, as an application, the plate that is most similar to the surrounding texture could be matched to fill the missing position due to the removal of defects. First, the image inpainting is divided into two stages: coarse inpainting and detailed inpainting. The coarse generator quickly generates the prediction result of the missing area, and the result is fed to the detailed generator to produce a more refined image. Then, the skip connection is applied to the classical Encoder–Decoder in order to solve the problem of information loss due to upsampling. Next, the attention block is introduced in the detailed generator to pay more attention to the meaningful semantics. Finally, normalizing missing and known regions together would result in a bias in the mean and standard deviation, so Partition Normalization was used instead of Batch Normalization. The feature map is divided into two parts, foreground and background, and the two parts are normalized separately. The experimental results show that the results of image inpainting are still high precision with less time spent on the training network. Code, demo and models are available at: https://github.com/NEFUJoeyChen/TG-Net .

Grounding electrode is an indispensable part in HVDC transmission projects. During its design and safety evaluation, step voltage is a crucial aspect. By reviewing and sorting out the present standards involving step voltage measurement and its safety limit, this paper illustrates the contradiction between the measurement methods and the safety evaluation. Reasons for the contradiction are explained by analyzing the principal of step voltage measurement and safety evaluation. The Open-circuit step voltage measurement method and the measurement method with a human body equivalent resistance paralleled are compared to find out their advantages and disadvantages. Suitable circumstances and corresponding safety criteria of these two methods are put forward. Method proposed in this paper can be applied to step voltage measurement and safety evaluation of HVDC grounding electrode, providing reference when revising the related standards.

Due to the dynamic characteristics, memristors have great potential for implementing various neural network training and applications. By applying memristors to neural networks as a strategy, the hardware architecture can be taken full advantage of to accelerate matrix operations. A mixed-kernel, variable-dimension memristive convolutional neural network (MixVMCNN), which can identify and classify six gases detected by the electronic nose, is proposed. To generate better gas recognition technique, we provide various receptive fields for the network by using different sizes of convolutional kernels in the same layer. We propose a novel variable-dimension convolutional approach that extracts features in different dimensions. Furthermore, we make use of memristor arrays to make the most of the hardware structure to speed up calculation. Experimental results indicate that the designed network, using only 56 weights, achieves the electronic nose gas classification with high accuracy of 99.72%, based on the Chemical gas sensor array dataset. To further exam the effectiveness of the model, supplemental experiments show that this model has stronger noise-robustness than other networks.

The conventional computing system with the architecture of von Neumann has greatly benefited our humans for past decades, while it is also suffered from low efficiency due to the separation between a memory block and a processing unit. Memristor, which is an emerging electron device with the capability of data storage and processing information simultaneously, can be employed to construct a bioinspired neuromorphic computing system. Simulation as one of the most powerful methods to obtain the optimizing result for the memristor-based neuromorphic network has been extensively focused to realize the high precision calculation. It becomes very difficult because the pulse-to-pulse (P2P) model is limited by the updating process. The memristor-based multi-layer Perceptron (MLP) network online training generally presents a low accuracy. Therefore, an efficiency training schedual is urgently desired to improve the accuracy. Based on the resistive switching behavior observed in the Ag/TiOx/F-doped SnO2 memristor, the weight update by the P2P model enables the MLP network online training in the low accuracy memristor with high performance. The low bits MLP optimized by a novel weight update schedual can realize high precision identification and classification. By that, the time and power consumption of memristor can be largely reduced. The experiment result illustrates that the high accuracy of 90.82% and 95.44% can be obtained at the first and final epoch of the MNIST handwritten digital datasets, respectively. Importantly, the number of the weight update, and the online training time and power consumption can be reduced by 81% and 93.7%, respectively. The scheme provides high precision, low power consumption, and fast convergence solution for the in-situ training of the imprecise memristor-based neuromorphic network.

Due to the impedance characteristic of conductor, the ground potential rise is different within grounding grid when impulse current is injected. When staff touches objects with different GPR at the same time, the GPD will be applied to human body, leading to potential personal safety problem. The impact of GPD on human body needs to be evaluated. The method of moment was used to calculate the ground potential rise of grounding grid under impulse current. By regarding the human body as a resistance, the current flowing through it because of ground potential difference was obtained. Methods based on current amplitude and electrical charge was used to evaluate personal safety. The effects of injecting current waveform, injecting position, soil resistivity, grounding conductor and grounding grid structure on ground potential difference and safe current threshold were studied. Results show the ground potential difference near injecting point may pose a threat to personal safety. The evaluation method based on electrical charge was recommended. In field test, the injecting point of grounding grid should be far away from corners, and staff should be kept away from the injecting point, so as to mitigate the risk of ground potential difference on personal safety.

Owing to its potential superiorities in terms of flexibility, compliance, and robustness, the musculoskeletal robotic system has become a promising direction for next-generation robots. However, motion learning and generalization of musculoskeletal systems are still challenging problems. In this article, a muscle-synergies-based neuromuscular control is proposed. First, a new computational model of time-varying muscle synergies is constructed, which utilizes both phasic and tonic muscle synergies to characterize the basic features of muscle excitations more sufficiently. Second, a novel neuromuscular control method is proposed for realizing the motion learning and generalization of musculoskeletal systems. Therein, a radial basis function (RBF) neural network is designed to modulate muscle synergies according to different movement targets. Muscle excitations are computed with the combination of modulated muscle synergies. Covariance matrix adaptation evolutionary strategy is applied to realize the synchronous optimization of muscle synergies and the RBF neural network. In the experiment, a sophisticated musculoskeletal system learns to perform center-out reaching tasks through trial-and-error learning on a few targets. With the muscle synergies and neural modulation learned from a few targets, the musculoskeletal system can also reach many unexperienced targets. The proposed method not only improves the speed and accuracy of motion learning but also enhances motion generalization. This article also promotes the development of the musculoskeletal robotic system and the fusion of neuroscience and robotics.

The visual complexity of interface plays a crucial role in user experience. Previous studies have extensively investigated website complexity, while the visual complexity of mobile applications gets seldom attention. This study explored the effect of interface complexity of mobile news apps on user search behaviors and satisfaction by dividing visual complexity into quantity complexity and layout complexity. Thirty-four subjects were invited to take part in the task-oriented eye-tracking experiment. The results revealed that a high level of quantity complexity increased users’ revisit counts, fixation counts, and saccade counts. A high level of layout complexity increased users’ revisit counts and saccade counts while fixation counts decreased first and then increased. Additionally, user satisfaction was negatively correlated with quantity complexity levels, while there was an inverted-U shape between user satisfaction and layout complexity levels. Understanding the effect of visual complexity of mobile news apps on user search behaviors and satisfaction would contribute to providing better guidelines for designers to develop mobile news products with an appropriate complexity layout and improve user satisfaction.

Compared with general joint-link robotic systems, bio-inspired musculoskeletal robotic systems offer the advantages of higher robustness, flexibility, and redundancy. Hence, they are a promising option for the development of next-generation robots. However, theoretical analysis regarding the superiorities of musculoskeletal systems is scarce. This study analyzes and proves the anti-interference of a musculoskeletal system both mathematically and experimentally. a) A new method to calculate the control signals of a highly nonlinear and deeply coupled musculoskeletal system is proposed, and the redundancy range is provided. The result shows that each movement of the system can be realized by multiple control signals in a subspace. b) The anti-interference to control signals caused by the nonlinear driving mechanism of muscles and the coupled feedback effect of the skeletal system are analyzed. The simulation results prove that the bio-inspired musculoskeletal robotic arm has a smaller tracking error than the joint-link robotic arm under the same perturbations of the control signals. The theoretical analysis is consistent with the simulation results, thereby providing a theoretical reference for the application of bio-inspired musculoskeletal robotic systems.

With the rapid development of remote sensing technologies, the frequency of observations of the same location is increasing, and many satellites and sensors produced a large amount of time series images. These images make long-term change detection and dynamic characteristic estimation of ground features possible. However, conventional remote sensing image change detection methods mostly rely on manual visual interpretation and supervised or unsupervised computer-aided classification. Traditional methods always face many bottlenecks when processing big and fast-growing datasets, such as low computational efficiency, low level of automation, and different identification standards and accuracies caused by different operators. With the rapid accumulation of remote sensing data, it has become an important but more challenging task to conduct change detection in a more precise, automated and standardized way. The development of geointelligent computing technologies provides a means of solving these problems and improve the accuracy and efficiency of remote sensing image change detection. In this paper, we presented a novel deep learning model called nest network(NestNet) based on a convolutional neural network to improve the accuracy of the automatic change detection task by using remotely sensed time series images. NestNet extracts the respective features of bi-temporal images using an encoding parallel module and subsequently employs absolute different operations to process the features of two images. Compared with change detection method based on U-Shaped network plus plus (UNet++), the parallel module improves the efficiency of NestNet. Finally, a decoding module is used to generate a predicted change image. This paper compares NestNet to traditional methods and state-of-the-art deep learning models on two datasets. The experimental results demonstrate that the accuracy of NestNet is better than that of state-of-the-art methods. It can be concluded that the NestNet model is a potential approach for change detection using high resolution remote sensing images.

The bearingless version of the induction motor (IM) has unacceptable performance for high speed or significant power applications. This is due to design challenges that are unique to the bearingless IM, including fundamental topology differences, rotor current induced by the suspension field, and a lack of techniques that can rapidly and accurately model the machine. This article presents a complete investigation into the design topology, modeling, and optimization of bearingless IMs to identify high-efficiency, power-dense designs for a high speed industrial compressor system. Key differences in the design of the bearingless IM from that of the classical line-fed IM are explored and an analytic design approach is proposed. A pole-specific rotor and a combined stator winding are used to improve the machine performance. Computationally efficient finite element modeling techniques are proposed and evaluated based on their ability to accurately calculate bearingless IM design performance metrics. An optimization framework is developed around these advancements using the multiobjective evolutionary algorithm based on decomposition. This article exercises this framework to explore the design space of four different slot-pole combination bearingless IMs for a 50 kW, 30 000 r/min compressor. High performance designs are identified that achieve $>96\%$ efficiency with a torque density competitive with high performance servo motors that use contact bearings.

Even though humanoid robots are being applied to diverse areas, the formation of users’ preference for the appearance of humanoid robots remains unknown. The present study investigated users’ neura...

It has been a long-term dream of roboticist to create a robot that has similar behavior and appearance to human. With highly redundant joints and actuators, the musculoskeletal system of human can fulfill movement and operation with high precision, flexibility, and reliability. However, the redundancy of the musculoskeletal system also brings great difficulties to the control and fabrication of a human-like robot. Therefore, we propose an algorithm based on the convex hull theory to effectively reduce the number of redundant muscles. Specifically, taking the strength limitation and state-dependent characteristics of muscles into consideration, by analyzing the torque contribution of muscles for the discrete motion of joints, each muscle can be represented by a feature vector, and the convex hulls are formed by these vectors in high-dimensional space. By applying the proposed algorithm, the muscles corresponding to the vertexes of the convex hull are reserved while the redundant muscles within the convex hull are deleted, and a simplified model can be obtained. Sets of motion experiments demonstrate that the simplified model can achieve high-precision movement as a complete model. Based on the simplified model, a hardware platform of a robotic arm is constructed, which consists of six degrees of freedom and 11 pneumatic muscles.

This article first shows that the advanced high-gain adaptive observer (AO) is able to estimate both speed and resistances during speed transients. However, it is also found that high-gain AO-based resistances estimation begins to diverge as the operating speed becomes higher. The problem is due to the fact that the model sensitivity of speed is much higher than that of resistances at high speeds. As a result, this article claims that the high-gain AO is not generally a good option to compensate parameters uncertainties in sensorless induction motor drives for all working conditions. As an alternative, this article proposes a scheme that not only has the potential of high dynamic performance but also avoids the aforementioned “sensitivity problem.” Besides, interesting topics, such as speed-dependent resistance estimation, sensorless inertia identification, slow speed reversal test, and reduced-order natural speed observer, are all covered in this article. Effective experiment verifies the feasibility of the proposed scheme, which is deemed to have potentials for practical applications.

This article reviews bearingless motor technology for medium and high power applications. Historically, bearingless motors have resided in research settings and certain niche commercial applications. Significant literature has been dedicated to developing designs that feature highly specific geometry, restricted by applications with a unique set of needs, oftentimes with low power requirements. The first part of this article reviews existing and potentially new use-cases of magnetic levitation in transportation and industrial applications. The second part of the article reviews bearingless motor technology for these applications, presents a comprehensive literature review of prototype test results, and highlights efforts to generalize and increase the power rating of bearingless motor technology. The final part of this article derives sizing equations of different bearingless motor topologies, which are summarized as a design tool for developing industrial-scale bearingless motor technology.

Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree search (MCTS) algorithm. The MCTS algorithm enables the generation model to provide a reward value that reflects the probability of generative examples bypassing the detector. To guarantee the antagonism and feasibility of the generative adversarial examples, the bypassing rules are restricted. The experimental results indicate that the missed detection rate of adversarial examples is significantly improved after the MCTS-T generation algorithm. Additionally, we construct a generative adversarial network (GAN) to optimize the detector and improve the detection rate when dealing with adversarial examples. After several epochs of adversarial training, the accuracy of detecting adversarial examples is significantly improved.

This brief addresses the adaptive observer design problem for sensorless induction motor (IM) drives with unknown resistances. In the literature, difficulty is found when applying several well-developed adaptive observer designs to IM dynamics. Hence, by introducing a parameter-dependent transformation, in the new coordinate, the IM dynamics manifest the attributes that facilitate the adaptive observer design. Although the classic designs that require linear-in-parameters result in overparameterization, the proposed nonlinearly parameterized adaptive observer demonstrates a good performance, as illustrated by the simulation and experimental results.

Due to easy implementation and fast dynamic response, deadbeat predictive current control (DPCC) has been got wide attention for permanent magnet synchronous machine (PMSM) drives. In order to further enhance DPCC performance, it is meaningful to compensate the voltage distortion caused by inverter nonlinearity. In this paper, an internal model control (IMC) is employed to constitute a voltage distortion estimator. In addition, the DPCC with the voltage distortion estimator is proposed. Compared with other compensation methods, the proposed method can be utilized for different PMSM speed and dc-bus voltage conditions. To verify the effectiveness of the proposed method, experimental results are carried out in this paper.

This paper reviews sensorless algorithms for both induction motors and permanent magnet motors using the active flux model, such that any design applicable for non-salient pole ac motors can also be included in the review framework. The proposed review framework classifies all sensorless algorithms following a five-layer hierarchy abbreviated as O-I-M-A-I, resulting in four main categories as i) inherently sensorless position estimation, ii) non-inherently sensorless position estimation, iii) post-position-estimation speed estimation, and iv) speed estimation for indirect field orientation. Various ac motor models are derived by assuming a constant active flux amplitude, based on which seven generic sensorless algorithms are summarized in a tutorial. Recommendations are made for sensorless drive designers to begin with inherently sensorless method such that the two-way coupling between position estimation and speed estimation is avoided. Finally, classical induction motor model results from time-varying active flux amplitude and slip relation, for which a state transformation is recommended for achieving global stability.

Aiming at the polarity optimization issue of Ternary fixed-polarity Reed-Muller (FPRM) circuits, a polarity optimization scheme for Ternary FPRM circuits based on multi-objective artificial mayfly algorithm (Multi-objective artificial mayfly algorithm, MOAMA) is proposed. First, a reverse mapping mechanism is introduced into the MOAMA algorithm to establish the mapping relationship between the positions of male and female mayflies. Secondly, artificial mayfly is set up to replace individuals with artificial mayfly with more evolutionary times but poor polarity. Finally, combining with Ternary FPRM expression, polarity conversion technology and circuit area and delay estimation model, the MOAMA algorithm is applied to Ternary FPRM circuit area and delay optimization. Simulation experiments were carried out on 10 MCNC Benchmark circuits, and the MOAMA algorithm was compared with the MODPSO algorithm and the MODCPSO algorithm. The results verified the effectiveness and reliability of the scheme.

Bi-level programming is widely used in processing various questions, but it cannot deal with the complex and fuzzy information contained in problems. In order to solve such problems better with intricate and vague information that can be efficiently handled by bifuzzy theory, a bifuzzy–bilevel programming model that sets the parameters to bifuzzy variables is proposed in this paper, which can process complex realistic data more accurately and improve the feasibility and validity of bi-level programming models. To ensure the solvability of the model, the equivalent form of the bifuzzy–bilevel programming model is obtained by utilizing the expected value operator. According to the linear and nonlinear characteristics of the model, the Karush–Kuhn–Tucker condition and particle swarm optimization algorithm are employed to handle the problem, respectively. Finally, by taking the distribution center location problem of the supplier as an example, the bifuzzy–bilevel programming model is applied in practice to balance highly intricate customer demands and corporate cost minimization, obtaining the feasible solution of functions at the upper and lower levels, and the bifuzzy information in the problem can also be processed well, which proves the effectiveness of the proposed methodology.

This paper addresses the problem of stator and rotor resistances identification in speed sensorless induction motor (IM) drives. The IM model that is transformable into the adaptive observer form is sought, for which the speed and rotor resistance adaptive observer can be established with rigorous stability arguments under the assumption of constant unknown parameters. However, further inclusion of stator resistance estimation leads to the overparameterization problem. As a result, the adaptive observer based on the first-order approximation of the error dynamics is proposed. This is feasible because the regressors of the adopted IM model consist of known signals only. An experiment is carried out to verify the effectiveness of a sensorless drive with the proposed adaptive observer. Compared with the existing methods, estimation of speed and resistances during a regeneration mode as well as successful slow-speed reversal operation is found possible in the experiments.

Human can accomplish manipulation with high precision, flexibility and robustness. As a musculoskeletal system naturally possesses superiorities of flexibility and robustness, realizing human-like manipulation with the musculoskeletal system is investigated in this paper. However, the redundancy, coupling and strong nonlinearity of musculoskeletal system will bring many challenges for control. Therefore, based on strategies of human manipulation and muscle coordination, a biologically inspired control scheme is proposed. First, we apply a strategy based on attractive region in environment to plan the manipulation under environmental constraints in the task space. Then, we extract muscle synergies as movement primitives and design an iterative learning controller to coordinate these synergies to accomplish complex manipulation. The muscle synergy based iterative learning controller can overcome the difficulty of constructing an explicit model of the sophisticated musculoskeletal system and improve motion learning by transforming the high dimensional muscle excitation into a lower dimensional space. With the combination of strategy based on attractive region in environment and muscle synergy based controller, we preliminarily realize human-like manipulation with high precision, flexibility and robustness. The effectiveness of our control scheme is verified in a peg-in-hole assembly task.

To meet the demand of the development of floating wind turbines, coupled aero-hydro-servo-elastic methods were developed and then were programmed as an integrated code DARwind (short for Dynamic Analysis for Response of Wind Turbines) for simulating floating wind turbines. This paper first presents the theoretical background, including Kane's dynamical equations in combination with the Cardan angles method, the hybrid coordinate dynamic analysis method, and the adjacent array approach for kinematics and kinetics. The blade element/momentum method with aerodynamic corrections was used for aerodynamic simulation. Potential-flow theory, the second-order wave forces and the Morison formula with the strip theory were used for hydrodynamics, and a quasi-static mooring modelling approach was developed for the catenary mooring system. A generator-torque controller and a full-span rotor-collective blade-pitch controller were adopted for control strategies. The code was then verified by a series of code-to-experiment comparisons, including the mooring system, the structural elasticity, the aerodynamic performance, the hydrodynamic performance and the control strategy. The comparisons demonstrated that the coupled aero-hydro-servo-elastic methods have a satisfactory ability to perform fully coupled simulations for floating wind turbines.

This letter responds to one question raised in the literature: whether or not global stability of speed estimation is possible to attain for sensorless induction motor drives. The answer is affirmative, excluding the zero-frequency operation that corresponds to the unobservable set of motor speed.

Machine Reading Comprehension (MRC) is a technique to make machine answer questions corresponding to the given passage. However, the existing MRC systems confront challenges to deal with the complex real-world fine-grained question answering tasks. To address the issue, we propose a novel method named TKB\(^2\)ert that generates prior knowledge data and utilizes it in behavioral fine-tuning at the first stage and trains itself through the dual attentive streams at the second stage. Subsequently, we take part in the MRC track of 2021 Language and Intelligence Challenge and win the first place. The result on the newly-built fine-grained MRC competition dataset validates the effectiveness of TKB\(^2\)ert.

Counterfactuals, serving as one of the emerging type of model interpretations, have recently received attention from both researchers and practitioners. Counterfactual explanations formalize the exploration of ``what-if'' scenarios, and are an instance of example-based reasoning using a set of hypothetical data samples. Counterfactuals essentially show how the model decision alters with input perturbations. Existing methods for generating counterfactuals are mainly algorithm-based, which are time-inefficient and assume the same counterfactual universe for different queries. To address these limitations, we propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models. We first analyze the model-based counterfactual process and construct a base synthesizer using a conditional generative adversarial net (CGAN). To better approximate the counterfactual universe for those rare queries, we novelly employ the umbrella sampling technique to conduct the MCS framework training. Besides, we also enhance the MCS framework by incorporating the causal dependence among attributes with model inductive bias, and validate its design correctness from the causality identification perspective. Experimental results on several datasets demonstrate the effectiveness as well as efficiency of our proposed MCS framework, and verify the advantages compared with other alternatives., Comment: 11 pages in total; To appear in KDD'21

In this paper, the modeling and position control problem of a class of pneumatic artificial muscle (PAM) is investigated for the bio-inspired robotic manipulator. The system model of PAM is firstly developed based on experiment data identification. Then, the improved particle swarm optimization (PSO) control parameter tuning method is established for the effective position control of PAM. In particular, the concept of information entropy is used during the optimal procedure. In the end, the simulation results are given for demonstrating the effectiveness of the proposed design and control approach.

In this paper, we propose a new unsupervised attention-based cycle generative adversarial network to solve the problem of single-image dehazing. The proposed method adds an attention mechanism that can dehaze different areas on the basis of the previous generative adversarial network (GAN) dehazing method. This mechanism not only avoids the need to change the haze-free area due to the overall style migration of traditional GANs, but also pays attention to the different degrees of haze concentrations that need to be changed, while retaining the details of the original image. To more accurately and quickly label the concentrations and areas of haze, we innovatively use training-enhanced dark channels as attention maps, combining the advantages of prior algorithms and deep learning. The proposed method does not require paired datasets, and it can adequately generate high-resolution images. Experiments demonstrate that our algorithm is superior to previous algorithms in various scenarios. The proposed algorithm can effectively process very hazy images, misty images, and haze-free images, which is of great significance for dehazing in complex scenes.

Self-moving robots may soon be ubiquitous in urban spaces, thereby enabling a wide range of use cases. To increase their social acceptance, previous work has explored how non-humanoid robots can express emotional states through various modalities, such as lights and movement. However, the majority of research has been carried out in controlled lab experiments, raising questions about their ecological validity. In this work, we present the design of emotional expressions for an urban robot via low-resolution lighting patterns and motion cues. We present an in-the-wild evaluation using questionnaires and interviews (N=72). Our findings suggest that a range of contextual aspects can influence the perception of the robot and its emotional expressions, such as overall choreography and interactional context, immediate impact of the environment, coupled with people's own emotions and past experiences. Our analysis thus outlines challenges and provides guidance for designers and researchers working with urban robots.

Aiming at the problem that it is difficult to diagnose mechanical faults of high-voltage circuit breakers using sound signals, a circuit breaker fault diagnosis method based on Local Mean Decomposition (LMD) and Extreme Learning Machine (ELM) is proposed. Use the laboratory 10kV indoor vacuum high voltage circuit breaker to perform sound closing data when the closing operation is normal, the screws are loose, the transmission mechanism is jammed, and the closing spring has insufficient energy storage, and the collected data is wavelet denoised and the LMD decomposition of the denoised signal is reconstructed according to the correlation coefficient, and the multi-scale entropy of the reconstructed signal is calculated as the feature vector. The feature vector set is divided into a training set and a test set and input to a limit learning machine for model training and fault classification. The experiments show that the LMD-ELM based high voltage circuit breaker mechanical fault diagnosis method can effectively identify circuit breaker mechanical faults in different states, and provides a new idea for circuit breaker mechanical fault diagnosis.

We introduce a data management problem called metadata debt, to identify the mapping between data concepts and their logical representations. We describe how this mapping can be learned using semisupervised topic models based on low-rank matrix factorizations that account for missing and noisy labels, coupled with sparsity penalties to improve localization and interpretability. We introduce a gauge transformation approach that allows us to construct explicit associations between topics and concept labels, and thus assign meaning to topics. We also show how to use this topic model for semisupervised learning tasks like extrapolating from known labels, evaluating possible errors in existing labels, and predicting missing features. We show results from this topic model in predicting subject tags on over 25,000 datasets from Kaggle.com, demonstrating the ability to learn semantically meaningful features., Comment: 8 pages, 4 figures. Data set available in paper source

This paper mainly discusses the hybrid application of ensemble learning, classification, and feature selection (FS) algorithms simultaneously based on training data balancing for helping the proposed credit scoring model perform more effectively, which comprises three major stages. Firstly, it conducts preprocessing for collected credit data. Then, an efficient feature selection algorithm based on adaptive elastic net is employed to reduce the weakly related or uncorrelated variables to get high-quality training data. Thirdly, a novel ensemble strategy is proposed to make the imbalanced training data set balanced for each extreme learning machine (ELM) classifier. Finally, a new weighting method for single ELM classifiers in the ensemble model is established with respect to their classification accuracy based on generalized fuzzy soft sets (GFSS) theory. A novel cosine-based distance measurement algorithm of GFSS is also proposed to calculate the weights of each ELM classifier. To confirm the efficiency of the proposed ensemble credit scoring model, we implemented experiments with real-world credit data sets for comparison. The process of analysis, outcomes, and mathematical tests proved that the proposed model is capable of improving the effectiveness of classification in average accuracy, area under the curve (AUC), H-measure, and Brier’s score compared to all other single classifiers and ensemble approaches.

When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias. However, the remaining features still need to be tested for correlation with sensitive features, which can only be done with the knowledge of those features. We resolve this dilemma using a fully homomorphic encryption scheme, allowing model developers to train linear regression and logistic regression models and test them for possible bias without ever revealing the sensitive features in the clear. We demonstrate how it can be applied to leave-one-out regression testing, and show using the adult income data set that our method is practical to run., Comment: 8 pages

Julia is a programming language for the scientific community that combines features of productivity languages, such as Python or MATLAB, with characteristics of performance-oriented languages, such as C++ or Fortran. Julia's productivity features include: dynamic typing, automatic memory management, rich type annotations, and multiple dispatch. At the same time, Julia allows programmers to control memory layout and leverages a specializing just-in-time compiler to eliminate much of the overhead of those features. This paper details the design choices made by the creators of Julia and reflects on the implications of those choices for performance and usability.

After a technical review on the stator resistance and rotor resistance identification methods for speed sensorless induction motor drives, this paper presents a different solution with a new representation of induction motor dynamics. By virtue of this representation, an adaptive observer is designed. The key step is to introduce the corrective terms and the filtered regressors, which forces the error dynamics of the observer to approach the ones in a form to which the Kalman–Yakubovich lemma can be applied. Only stator currents are required in the parameter adaptation rules. Particularly, the biased sinusoidal flux modulus command assures the convergence of parameters, and the consequent speed ripple problem is discussed. However, the aforementioned design has an imperfection and is found to be insufficient to assure stable operation in low-speed regeneration. Thus, further stability analysis based on the steady-state linearized model and remedies for stabilization are given. Effective experiment and simulation results are included.

Recently, the open review process has attracted the attention of more and more researchers. In order to deeply and quantitatively understand the process, we employ statistical analysis methods, combining human dynamics theory with the assortative mixing analysis of networks to study the process. Our results present the burst characteristics and preference characteristics of authors and reviewers during the review process. Simultaneously, these results demonstrate the irrelevance between the popularity of the manuscript research topic and the manuscript acceptance rate. Moreover, using the assortative mixing analysis method confirmed that the keywords network basically has obvious assortativity. Furthermore, these results suggest that although no inevitable connection between keywords and the acceptance rate of the manuscript, the combination of keywords in the manuscript is influential. Our research results provide useful points for the design of the review system and paper submission. Future work should analyze more data from other disciplines or deconstruct the underlying mechanism of the open review process.

This paper presents a framework for the optimal design of the bearingless induction motor intended for industrial compressor applications. A pole-specific rotor winding and a combined stator winding are used to improve the machine performance. An initial design is constructed by following a standard design procedure for AC machines. Design considerations and motor performance evaluation that are unique to the bearingless induction motor are discussed. The 50 kW, 30,000 r/min bearingless induction motor is optimized using multi-objective evolutionary algorithm based on decomposition (MOEA/D). The Pareto front is shown in terms of torque density, efficiency, and ripple performance. Three optimal designs are selected according to different criteria. The improvement margin and tolerance of an optimal design is further investigated through a local sensitivity analysis. Typical optimized designs are found to have an efficiency of around 95.4% using 29-gauge M-19 steel and require less than 1% of the slot space for suspension current to levitate the rotor shaft.

Accurate detection of network-based attacks is crucial to prevent security breaches of information systems. The recent application of deep learning approaches for network intrusion detection has shown promising. However, the challenges remain on how to deal with imbalance data and small samples as well as reducing false alarm rate (FAR). To address these issues, this work has proposed a multiple-layer representation learning model for accurate end-to-end network intrusion detection by combining deep convolutional neural networks (CNN) with gcForest. The contributions of this work lie in 1) a new data encoding scheme based on P-Zigzag to encode network traffic data into two-dimensional gray-scale images for representation learning without loss of original information; 2) The combination of gcForest and CNN allows accurate detection on imbalanced data and small scale data with fewer hyperparamters comparing to most existing deep learning models, which increase computational efficiency. The proposed approach is based on a multiple-layer approach consisting of a coarse layer and a fine layer, in which the coarse layer with the improved CNN model (GoogLeNetNP) focuses on identification of N abnormal classes and a normal class. While in the fine layer, an improved model based on gcForest (caXGBoost) further classifies the abnormal classes into N-1 subclasses. This ensures fine-grained detection of various attacks. The proposed framework has been compared with the existing deep learning models using three real datasets (a new dataset NBC, a combination of UNSW-NB15 and CICIDS2017 consisting of 101 classes). The experimental results show that our proposed method outperforms other single deep learning methods (i.e., AlexNet, VGG19, GoogleNet, InceptionV3, ResNet18) in terms of accuracy, detection rate, and FAR, which demonstrates its effectiveness in detecting fine-grained attacks and handling imbalanced datasets with high-precision and low FAR.

Redundant muscles in human-like musculoskeletal robotics bring extra dimensions to solution space, which makes the computation of muscle excitation become an open question. Conventional methods like dynamic optimization and reinforcement learning usually have a high computational cost or an unstable learning process when they are applied into a complex musculoskeletal system. In this paper, inspired by the learning process of human, we build a phased target learning framework which provides different targets to learners from different levels to guide their training process and avoid local optima. By introducing an extra layer of neurons with preference, we improved the Q-network method to generate continuous excitation. In addition, based on the process of information transmission in human nervous system, two kinds of biological noises are designed in our algorithm to enhance exploration ability of our method in solution space. Tracking experiments based on a simplified musculoskeletal arm model indicate that under the guiding of phased targets, our method avoids divergence of excitation and obtains a stable training process. Besides, with its enhanced ability of exploration, our method shows smaller motion errors during the motion. Importantly, the phased target learning framework can be expanded as a general reinforcement learning framework, and it is a preliminary interpretation for modeling human motion learning manners.

This paper presents a 5.75 GHz high-resolution continuous wave Doppler bioradar system based on the analog correlator. It is used to detect the breathing and heartbeat signals of a stationary human body. To reduce the power consumption of the system, the quadrature demodulation receiver is replaced by the analog correlator to generate the quadrature baseband signal. A radio frequency leakage cancellation module based on the six-port network is used at the front end to improve the detection sensitivity and the Tx-Rx isolation. The results show that the bioradar can accurately measure the heartbeat and respiratory rates at a distance of 1.5 meters.

The bearingless version of the induction motor (IM) has seen little consideration as a high performance, medium to high power motor. This paper investigates and develops design and performance evaluation techniques that are necessary to systematically study and design the bearingless IM. Key differences in the design considerations of the bearingless IM from that of the classical line-fed or inverter-fed 1M are first explored and a candidate design (4 poles, 15,000 rpm, 50 kW) is created using a modified analytic design approach. Finite element analysis (FEA) is used to explore the impact of the rotor topology (squirrel cage or pole-specific rotor winding) on the machine performance. It is shown that for low speed designs, the traditionally-used cage rotor can yield acceptable performance. However, the cage rotor is found to not be a workable solution for high speed designs (an example design operated at 500 Hz requires 20% of rated stator current to merely support the shaft weight). Finally, several FEA techniques are proposed and evaluated based on their ability to rapidly and accurately evaluate the bearingless IM design performance. This investigation will provide the necessary foundation for future efforts in designing and optimizing the bearingless IM.

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to impute these missing labels for compliance assessments. Empirically, these methods are observed to exaggerate disparities, but the reason why is unknown. In this paper, we decompose the biases in estimating outcome disparity via threshold-based imputation into multiple interpretable bias sources, allowing us to explain when over- or underestimation occurs. We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership. Finally, we illustrate our results with numerical simulations and a public dataset of mortgage applications, using geolocation as a proxy for race. We confirm that the bias of threshold-based imputation is generally upward, but its magnitude varies strongly with the threshold chosen. Our new weighted estimator tends to have a negative bias that is much simpler to analyze and reason about., 13 pages, 11 figures, FAT*' 19: Conference on Fairness, Accountability, and Transparency (FAT*' 19), January 29-31, 2019, Atlanta, GA, USA

For a single-phase distribution network static synchronous compensator (DSTATCOM) based on cascaded H-bridge (CHB) converter, the design of dq control-based system is difficult due to the coupling effect and current fiction. To address this issue, first, the mathematical model of dq control is derived, then the unit vector (UV) control based on αβ frame is proposed. In this paper, the compensation current reference is obtained through the instantaneous power theory and unit vector method. Proposed control structure eliminates the effects of the decoupling control and the complex current fiction. In addition, the DC-voltage balance is achieved. Finally, the effectiveness of the proposed control strategy is verified by the simulation and experiments.

Many end-to-end, large vocabulary, continuous speech recognition systems are now able to achieve better speech recognition performance than conventional systems. Most of these approaches are based on bidirectional networks and sequence-to-sequence modeling however, so automatic speech recognition (ASR) systems using such techniques need to wait for an entire segment of voice input to be entered before they can begin processing the data, resulting in a lengthy time-lag, which can be a serious drawback in some applications. An obvious solution to this problem is to develop a speech recognition algorithm capable of processing streaming data. Therefore, in this paper we explore the possibility of a streaming, online, ASR system for Japanese using a model based on unidirectional LSTMs trained using connectionist temporal classification (CTC) criteria, with local attention. Such an approach has not been well investigated for use with Japanese, as most Japanese-language ASR systems employ bidirectional networks. The best result for our proposed system during experimental evaluation was a character error rate of 9.87%.

By means of the electromagnetic simulation software, the cable model of the electric equipment inside the locomotive is established. The electromagnetic field of different types of cables, the situation of different positions and whether to add barriers are analyzed, so that the shielding effect can be reflected. Besides, the formula of shielding effectiveness is analyzed and the relevant conclusions are obtained. Through this paper, a conclusion can be drawn about the influence of different cable types, different cable placement methods, and whether or not to add barriers between cables on electromagnetic compatibility.

Reducing the global diseases burden requires effective diagnosis and treatment. In the developing world, accurate diagnosis can be the most expensive and time-consuming aspect of health care. Healthcare cost can, however, be reduced by use of affordable rapid diagnostic tests (RDTs). In the developed world, low-cost RDTs are being developed in many research laboratories; however, they are not being equally adopted in the developing countries. This disconnect points to a gap in the design philosophy, where parameterization of design variables ignores the most critical component of the system, the point-of-use stakeholders (e.g., doctors, nurses and patients). Herein, we demonstrated that a general focus on reducing cost (i.e., “low-cost”), rather than efficiency and reliability is misguided by the assumption that poverty reduces the value individuals place on their well-being. A case study of clinicians in Kenya showed that “zero-cost” is a low-weight parameter for point-of-use stakeholders, while reliability and standardization are crucial. We therefore argue that a user-driven, value-addition systems-engineering approach is needed for the design of RDTs to enhance adoption and translation into the field.

To study the characteristics and performance of the deep learning in intelligent intrusion detection, two hybrid algorithms, which combine restricted Boltzmann machine (RBM) with support vector machine (SVM) and deep belief network (DBN) respectively, are used to analyze the accuracy, false positive rate, false negative rate and testing time with the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition (KDDCup99). Compared with each other and traditional hybrid intrusion detection algorithm, DBN performs better than the other both in the accuracy and speed, which is attributed to the unsupervised learning of RBM networks and the combination of the neural networks at the bottom.